利用支持sla的遗传算法降低云数据中心虚拟机整合的成本

Hossein Monshizadeh Naeen
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引用次数: 1

摘要

云计算是一种利用网络设施来提供、使用和交付计算服务的计算模式。如今,降低能源消耗的问题与云服务提供商的效率一起变得非常重要。动态虚拟机(VM)整合是一种在云数据中心中用于节能计算的技术。在本文中,我们提供了降低总成本的解决方案,包括能源消耗和服务水平协议(SLA)违反。为了将虚拟机整合到数量较少的物理机中,提出了一种基于遗传算法的感知sla的虚拟机放置方法。为了使虚拟机放置算法能够感知sla,该方法将工作负载视为非平稳随机过程,并使用一种新的动态滑动窗口算法将其自动逼近为平稳过程。CloudSim工具包中的模拟结果证实,本文提出的虚拟服务器整合算法提供了显着的总成本节约(通过ESV度量进行评估),比最佳基准算法高出约45%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cost Reduction Using SLA-Aware Genetic Algorithm for Consolidation of Virtual Machines in Cloud Data Centers
—Cloud computing is a computing model which uses network facilities to provision, use and deliver computing services. Nowadays, the issue of reducing energy consumption has become very important alongside the efficiency for Cloud service providers. Dynamic virtual machine (VM) consolidation is a technology that has been used for energy efficient computing in Cloud data centers. In this paper, we offer solutions to reduce overall costs, including energy consumption and service level agreement (SLA) violation. To consolidate VMs into a smaller number of physical machines, a novel SLA-aware VM placement method based on genetic algorithms is presented. In order to make the VM placement algorithm be SLA-aware, the proposed approach considers workloads as non-stationary stochastic processes, and automatically approximates them as stationary processes using a novel dynamic sliding window algorithm. Simulation results in the CloudSim toolkit confirms that the proposed virtual server consolidation algorithms in this paper provides significant total cost savings (evaluated by ESV metric), which is about 45% better than the best of the benchmark algorithms.
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